Grade-specific diagnostic and prognostic biomarkers in breast cancer

Overexpression of CDC25A, AURKB, and TOP2A Genes Could Be an Important Clue for Luminal A Breast Cancer

Objective

Breast cancer (BC) is highly heterogeneous and one of the most common cancers. Luminal A (LUM A) is a subtype of BC with a better prognosis than other BC subtypes. The molecular mechanisms underlying the initiation and progression of the LUM A subtype are still unclear. Big data generated from microarray and sequencing systems can be re-analyzed, especially with the help of various in silico tools developed in recent years, and made applicable for in vitro and in vivo research. This work aimed to identify genes that may play a role in the progression of LUM A subtype of BC using both computational and laboratory-based methods.

Materials and Methods

Overlapping genes associated with BC were identified from the The Cancer Genome Atlas database, GSE233242, GSE100925 geodata sets, and the geneshot tool. The network functional analysis between overlapping genes was determined with STRING 12.0. Expression levels of overlapping genes in BC were investigated with the TNMplot (https://tnmplot.com/analysis/) in silico tool. The effect of overlapping genes on the overall survival of LUM A cancer patients was defined using the Kaplan-Meier plotter tool. Expressions of genes identified using bioinformatics data were investigated via quantitative real-time -polymerase chain reaction (qRT-PCR) in LUM A tumor and adjacent tissue samples. The data were evaluated using the t-test. Both the sensitivity and specificity of selected genes have been determined using the receiver operating characteristic curve.

Results

In silico investigation showed that eleven genes were possibly associated with BC. Among them CDC25A, AURKB, and TOP2A were considerably increased in LUM A samples according to qRT-PCR results. An overall survival analysis also showed that overexpression of these three genes could reduce the overall survival of LUM A patients.

Conclusion

The genes CDC25A, AURKB, and TOP2A may play crucial functions in LUM A pathogenesis. Therapeutic strategies that diminish the expression of these connected genes may enhance the prognosis of LUM A patients.

KIF2C/MCAK a prognostic biomarker and its oncogenic potential in malignant progression, and prognosis of cancer patients: a systematic review and meta-analysis as biomarker

KIF2C/MCAK (KIF2C) is the most well-characterized member of the kinesin-13 family, which is critical in the regulation of microtubule (MT) dynamics during mitosis, as well as interphase. This systematic review briefly describes the important structural elements of KIF2C, its regulation by multiple molecular mechanisms, and its broad cellular functions. Furthermore, it systematically summarizes its oncogenic potential in malignant progression and performs a meta-analysis of its prognostic value in cancer patients. KIF2C was shown to be involved in multiple crucial cellular processes including cell migration and invasion, DNA repair, senescence induction and immune modulation, which are all known to be critical during the development of malignant tumors. Indeed, an increasing number of publications indicate that KIF2C is aberrantly expressed in multiple cancer entities. Consequently, we have highlighted its involvement in at least five hallmarks of cancer, namely: genome instability, resisting cell death, activating invasion and metastasis, avoiding immune destruction and cellular senescence. This was followed by a systematic search of KIF2C/MCAK's expression in various malignant tumor entities and its correlation with clinicopathologic features. Available data were pooled into multiple weighted meta-analyses for the correlation between KIF2Chigh protein or gene expression and the overall survival in breast cancer, non-small cell lung cancer and hepatocellular carcinoma patients. Furthermore, high expression of KIF2C was correlated to disease-free survival of hepatocellular carcinoma. All meta-analyses showed poor prognosis for cancer patients with KIF2Chigh expression, associated with a decreased overall survival and reduced disease-free survival, indicating KIF2C's oncogenic potential in malignant progression and as a prognostic marker. This work delineated the promising research perspective of KIF2C with modern in vivo and in vitro technologies to further decipher the function of KIF2C in malignant tumor development and progression. This might help to establish KIF2C as a biomarker for the diagnosis or evaluation of at least three cancer entities.

Prognostic value of RNA methylation-related genes in gastric adenocarcinoma based on bioinformatics

Background

Gastric cancer (GC) is a malignant tumor that originates from the epithelium of the gastric mucosa and has a poor prognosis. Stomach adenocarcinoma (STAD) covers 95% of total gastric cancer. This study aimed to identify the prognostic value of RNA methylation-related genes in gastric cancer.

Methods

In this study, The Cancer Genome Atlas (TCGA)-STAD and GSE84426 cohorts were downloaded from public databases. Patients were classified by consistent cluster analysis based on prognosis-related differentially expressed RNA methylation genes Prognostic genes were obtained by differential expression, univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses. The prognostic model was established and validated in the training set, test set and validation set respectively. Independent prognostic analysis was implemented. Finally, the expression of prognostic genes was affirmed by reverse transcription quantitative PCR (RT-qPCR).

Results

In total, four prognostic genes (ACTA2, SAPCD2, PDK4 and APOD) related to RNA methylation were identified and enrolled into the risk signature. The STAD patients were divided into high- and low-risk groups based on the medium value of the risk score, and patients in the high-risk group had a poor prognosis. In addition, the RNA methylation-relevant risk signature was validated in the test and validation sets, and was authenticated as a reliable independent prognostic predictor. The nomogram was constructed based on the independent predictors to predict the 1/3/5-year survival probability of STAD patients. The gene set enrichment analysis (GSEA) result suggested that the poor prognosis in the high-risk subgroup may be related to immune-related pathways. Finally, the experimental results indicated that the expression trends of RNA methylation-relevant prognostic genes in gastric cancer cells were in agreement with the result of bioinformatics.

Conclusion

Our study established a novel RNA methylation-related risk signature for STAD, which was of considerable significance for improving prognosis of STAD patients and offering theoretical support for clinical therapy.

KIF2C: An important factor involved in signaling pathways, immune infiltration, and DNA damage repair in tumorigenesis

BACKGROUNDS

Poorly regulated mitosis and chromosomal instability are common characteristics in malignant tumor cells. Kinesin family member 2 C (KIF2C), also known as mitotic centromere-associated kinesin (MCAK) is an essential component during mitotic regulation. In recent years, KIF2C was shown to be dysregulated in several tumors and was involved in many aspects of tumor self-regulation. Research on KIF2C may be a new direction and target for anti-tumor therapy.

OBJECT

The article aims at reviewing current literatures and summarizing the research status of KIF2C in malignant tumors as well as the oncogenic signaling pathways associated with KIF2C and its role in immune infiltration.

RESULT

In this review, we summarize the KIF2C mechanisms and signaling pathways in different malignant tumors, and briefly describe its involvement in pathways related to classical chemotherapeutic drug resistance, such as MEK/ERK, mTOR, Wnt/β-catenin, P53 and TGF-β1/Smad pathways. KIF2C upregulation was shown to promote tumor cell migration, invasion, chemotherapy resistance and inhibit DNA damage repair. It was also highly correlated with microRNAs, and CD4 +T cell and CD8 +T cell tumor immune infiltration.

CONCLUSION

This review shows that KIF2C may function as a new anticancer drug target with great potential for malignant tumor treatment and the mitigation of chemotherapy resistance.

Stage-specific coexpression network analysis of Myc in cohorts of renal cancer

The present study investigates the molecular dynamics of Myc in normal precursors and in different stages (I/II/III/IV) of cohorts of renal cancer using two distinct yet complementary approaches: gene expression and gene coexpression. We also analysed the variation of coexpression networks of Myc through the stage-wise progression of renal cancer cohorts. Myc expression is significantly higher in stage I compared to normal tissue but changed inconsistently across stages of renal cancer. We identified that Myc consistently coexpressed with fourteen genes in the KIPAN [Pan-kidney cohort (KICH + KIRC + KIRP)] and eight in the KIRC (Kidney renal clear cell carcinoma) across all stages, providing potential prognostic and diagnostic biomarkers. Coexpression network complexity decreased from normal precursor tissues to associated tumour stage I in KIPAN and KIRC but was inconsistent after that. In the process of cancer development, there is generally lower cross-tissue cancer network homology observed among coexpressed genes with Myc during the normal to the stage I compared to the stage-wise progression of cancer. Overall, this research provides novel perceptions of the molecular causes of kidney cancer. It also highlights potential genes and pathways crucial for diagnosing and treating this disease.

KIF2C is a prognostic biomarker associated with immune cell infiltration in breast cancer

BACKGROUND

The kinesin-13 family member 2C (KIF2C) is a versatile protein participating in many biological processes. KIF2C is frequently up-regulated in multiple types of cancer and is associated with cancer development. However, the role of KIF2C in immune cell infiltration of tumor microenvironment and immunotherapy in breast cancer remains unclear.

METHODS

The expression of KIF2C was analyzed using Tumor Immune Estimation Resource (TIMER) database and further verified by immunohistochemical staining in human breast cancer tissues. The correlation between KIF2C expression and clinical parameters, the impact of KIF2C on clinical prognosis and independent prognostic factors were analyzed by using TCGA database, the Kaplan-Meier plotter, and Univariate and multivariate Cox analyses, respectively. The nomograms were constructed according to independent prognostic factors and validated with C-index, calibration curves, ROC curves, and decision curve analysis. A gene set enrichment analysis (GSEA) was performed to explore the underlying molecular mechanisms of KIF2C. The degree of immune infiltration was assessed by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using the Expression (ESTIMATE) algorithm and the single sample GSEA (ssGSEA). The Tumor mutational burden and Tumor Immune Dysfunction and Rejection (TIDE) were used to analyze immunotherapeutic efficiency. Finally, the KIF2C-related competing endogenous RNA (ceRNA) network was constructed to predict the putative regulatory mechanisms of KIF2C.

RESULTS

KIF2C was remarkably up-regulated in 18 different types of cancers, including breast cancer. Kaplan-Meier survival analysis showed that high KIF2C expression was associated with poor overall survival (OS). KIF2C expression was associated with clinical parameters such as age, TMN stage, T status, and molecular subtypes. We identified age, stage, estrogen receptor (ER) and KIF2C expression as OS-related independent prognosis factors for breast cancer. An OS-related nomogram was developed based on these independent prognosis factors and displayed good predicting ability for OS of breast cancer patients. Finally, our results revealed that KIF2C was significantly related to immune cell infiltration, tumor mutational burden, and immunotherapy in patients with breast cancer.

CONCLUSION

KIF2C was overexpressed in breast cancer and was positively correlated with immune cell infiltration and immunotherapy response. Therefore, KIF2C can serve as a potential biomarker for prognosis and immunotherapy in breast cancer.

MACC1 as a Potential Target for the Treatment and Prevention of Breast Cancer

Metastasis associated in colon cancer 1 (MACC1) is an oncogene first identified in colon cancer. MACC1 has been identified in more than 20 different types of solid cancers. It is a key prognostic biomarker in clinical practice and is involved in recurrence, metastasis, and survival in many types of human cancers. MACC1 is significantly associated with the primary tumor, lymph node metastasis, distant metastasis classification, and clinical staging in patients with breast cancer (BC), and MACC1 overexpression is associated with reduced recurrence-free survival (RFS) and worse overall survival (OS) in patients. In addition, MACC1 is involved in BC progression in multiple ways. MACC1 promotes the immune escape of BC cells by affecting the infiltration of immune cells in the tumor microenvironment. Since the FGD5AS1/miR-497/MACC1 axis inhibits the apoptotic pathway in radiation-resistant BC tissues and cell lines, the MACC1 gene may play an important role in BC resistance to radiation. Since MACC1 is involved in numerous biological processes inside and outside BC cells, it is a key player in the tumor microenvironment. Focusing on MACC1, this article briefly discusses its biological effects, emphasizes its molecular mechanisms and pathways of action, and describes its use in the treatment and prevention of breast cancer.

Identification and validation of novel prognostic biomarkers and therapeutic targets for non-small cell lung cancer

Background: Despite the significant survival benefits of anti-PD-1/PD-L1 immunotherapy, non-small cell lung cancer (NSCLC) remains one of the most common tumors and major causes of cancer-related deaths worldwide. Thus, there is an urgent need to identify new therapeutic targets for this refractory disease.

Methods: In this study, microarray datasets GSE27262, GSE75037, GSE102287, and GSE21933 were integrated by Venn diagram. We performed functional clustering and pathway enrichment analyses using R. Through the STRING database and Cytoscape, we conducted protein-protein interaction (PPI) network analysis and identified the key genes, which were verified by the GEPIA2 and UALCAN portal. Validation of actin-binding protein anillin (ANLN) was performed by quantitative real-time polymerase chain reaction and Western blotting. Additionally, Kaplan-Meier methods were used to compute the survival analyses.

Results: In total, 126 differentially expressed genes were identified, which were enriched in mitotic nuclear division, mitotic cell cycle G2/M transition, vasculogenesis, spindle, and peroxisome proliferator-activated receptor signaling pathway. 12 central node genes were identified in the PPI network complex. The survival analysis revealed that high transcriptional levels were associated with inferior survival in NSCLC patients. The clinical implication of ANLN was further explored; its protein expression showed a gradually increasing trend from grade I to III.

Conclusion: These Key genes may be involved in the carcinogenesis and progression of NSCLC, which may serve as useful targets for NSCLC diagnosis and treatment.

UBE2C promotes leptomeningeal dissemination and is a therapeutic target in brain metastatic disease

Background

Despite current improvements in systemic cancer treatment, brain metastases (BM) remain incurable, and there is an unmet clinical need for effective targeted therapies.

Methods

Here, we sought common molecular events in brain metastatic disease. RNA sequencing of thirty human BM identified the upregulation of UBE2C, a gene that ensures the correct transition from metaphase to anaphase, across different primary tumor origins.

Results

Tissue microarray analysis of an independent BM patient cohort revealed that high expression of UBE2C was associated with decreased survival. UBE2C-driven orthotopic mouse models developed extensive leptomeningeal dissemination, likely due to increased migration and invasion. Early cancer treatment with dactolisib (dual PI3K/mTOR inhibitor) prevented the development of UBE2C-induced leptomeningeal metastases.

Conclusions

Our findings reveal UBE2C as a key player in the development of metastatic brain disease and highlight PI3K/mTOR inhibition as a promising anticancer therapy to prevent late-stage metastatic brain cancer.

Integrative Analysis of Potential Biomarkers Involved in the Progression of Papillary Thyroid Cancer

This study aims to explore key prognostic and diagnostic biomarkers involved in the pathogenesis of papillary thyroid cancer (PTC) which is one of the most common endocrine cancers and whose occurrence is rapidly increasing. Papillary thyroid cancer datasets containing normal and tumor samples were collected from Gene Expression Omnibus. Protein-protein interaction (PPI) network for common upregulated differentially expressed genes (DEGs) was constructed, and hub genes were studied. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed to identify the vital biological behaviors and pathways involved in PTC. PPI network analysis demonstrated the interaction between 134 common upregulated DEGs, and top 15 pivotal genes with highest degree of connectivity were retrieved. Three of the hub genes (DPP4, ITGA2, FN1) were linked to the prognosis of PTC patients and considered clinically relevant core genes via survival analysis. We suggest that the identification of key genes associated with PTC development help us in understanding molecular mechanisms related to disease. These genes could also be considered the diagnostic biomarkers or as therapeutic targets in the future treatment for PTC.

Game-theoretic link relevance indexing on genome-wide expression dataset identifies putative salient genes with potential etiological and diapeutics role in colorectal cancer

Diapeutics gene markers in colorectal cancer (CRC) can help manage mortality caused by the disease. We applied a game-theoretic link relevance Index (LRI) scoring on the high-throughput whole-genome transcriptome dataset to identify salient genes in CRC and obtained 126 salient genes with LRI score greater than zero. The biomarkers database lacks preliminary information on the salient genes as biomarkers for all the available cancer cell types. The salient genes revealed eleven, one and six overrepresentations for major Biological Processes, Molecular Function, and Cellular components. However, no enrichment with respect to chromosome location was found for the salient genes. Significantly high enrichments were observed for several KEGG, Reactome and PPI terms. The survival analysis of top protein-coding salient genes exhibited superior prognostic characteristics for CRC. MIR143HG, AMOTL1, ACTG2 and other salient genes lack sufficient information regarding their etiological role in CRC. Further investigation in LRI methodology and salient genes to augment the existing knowledge base may create new milestones in CRC diapeutics.

LINC01194 recruits NUMA1 to promote ubiquitination of RYR2 to enhance malignant progression in triple-negative breast cancer

Long intergenic nonprotein coding RNA 1194 (LINC01194) has been reported as an oncogene in several cancer types, but its expression and potential role in triple-negative breast cancer (TNBC) are still unclear. We found that LINC01194 was significantly highly expressed in TNBC based on The Cancer Genome Atlas (TCGA) database. Data from in vitro experiments and in vivo assays demonstrated that LINC01194 promoted TNBC progression. Through bioinformatics prediction, mass spectrometry, and mechanical experiments, we found that LINC01194 could recruit nuclear mitotic apparatus protein 1 (NUMA1) to bind to the untranslated region (3'UTR) of ubiquitin-conjugating enzyme E2 C (UBE2C) 3' and stabilize UBE2C mRNA. Moreover, we found that UBE2C acted as an ubiquitin ligase to promote the ubiquitination and degradation of ryanodine receptor type 2 (RYR2) that inhibited the progression of TNBC by inhibiting the Wnt/β-catenin signaling pathway. In summary, LINC01194 activate the Wnt/β-catenin signaling pathway and accelerates the malignant progression of TNBC by recruiting NUMA1 to stabilize UBE2C mRNA and thus promotes RYR2 ubiquitination and degradation. These findings might provide a more effective therapeutic strategy for TNBC patients.

PCMT1 Is a Potential Prognostic Biomarker and Is Correlated with Immune Infiltrates in Breast Cancer

Background

Protein-L-isoaspartate (D-aspartate) O-methyltransferase (PCMT1) is involved in the occurrence and development of a variety of malignant tumors. However, the prognostic value of PCMT1 in breast cancer remains unclear.

Methods

Based on the Cancer Genome Atlas database, we assessed the correlation between the expression of PCMT1 and prognosis, immune invasion, and tumor mutation burden in a variety of cancers. The expression level, mutation, immune correlation, and coexpression of PCMT1 in breast cancer were studied using the following databases: UALCAN database, Human Protein Atlas database, cBioPortal database, TIMER database, and LinkedOmics database. Kaplan–Meier Plotter was used for survival analysis. Receiver operating characteristic (ROC) curves and nomograms were drawn using the R software package. P < 0.05 was considered statistically significant.

Results

Pancancer analysis showed that PCMT1 is highly expressed in a variety of cancers and is significantly related to the prognosis of a variety of cancers. PCMT1 is significantly related to the tumor mutation burden of a variety of cancers. PCMT1 is significantly high in breast cancer, and it is significantly related to the abundance of immune infiltration. Survival analysis revealed that high PCMT1 expression is significantly associated with shorter overall survival (OS), relapse-free survival (RFS), and postprogression survival (PPS) in breast cancer patients. ROC curves and nomograms verify the effectiveness of PCMT1 as a prognostic biomarker for breast cancer.

Conclusions

PCMT1 can be used as a potential prognostic biomarker of breast cancer, and it is significantly related to the abundance of breast cancer immune infiltration.

A Deep Learning-Based Framework for Supporting Clinical Diagnosis of Glioblastoma Subtypes

Understanding molecular features that facilitate aggressive phenotypes in glioblastoma multiforme (GBM) remains a major clinical challenge. Accurate diagnosis of GBM subtypes, namely classical, proneural, and mesenchymal, and identification of specific molecular features are crucial for clinicians for systematic treatment. We develop a biologically interpretable and highly efficient deep learning framework based on a convolutional neural network for subtype identification. The classifiers were generated from high-throughput data of different molecular levels, i.e., transcriptome and methylome. Furthermore, an integrated subsystem of transcriptome and methylome data was also used to build the biologically relevant model. Our results show that deep learning model outperforms the traditional machine learning algorithms. Furthermore, to evaluate the biological and clinical applicability of the classification, we performed weighted gene correlation network analysis, gene set enrichment, and survival analysis of the feature genes. We identified the genotype-phenotype relationship of GBM subtypes and the subtype-specific predictive biomarkers for potential diagnosis and treatment.

Breast Cancer Characteristics in the Population of Survivors Participating in the World Trade Center Environmental Health Center Program 2002-2019

The destruction of World Trade Center on 11 September 2001 exposed local community members to a complex mixture of known carcinogens and potentially carcinogenic substances. To date, breast cancer has not been characterized in detail in the WTC-exposed civilian populations. The cancer characteristics of breast cancer patients were derived from the newly developed Pan-Cancer Database at the WTC Environmental Health Center (WTC EHC). We used the Surveillance, Epidemiology, and End Results (SEER) Program breast cancer data as a reference source. Between May 2002 and 31 December 2019, 2840 persons were diagnosed with any type of cancer at the WTC EHC, including 601 patients with a primary breast cancer diagnosis (592 women and 9 men). There was a higher proportion of grade 3 (poorly differentiated) tumors (34%) among the WTC EHC female breast cancers compared to that of the SEER-18 data (25%). Compared to that of the SEER data, female breast cancers in the WTC EHC had a lower proportion of luminal A (88% and 65%, respectively), higher proportion of luminal B (13% and 15%, respectively), and HER-2-enriched (5.5% and 7%, respectively) subtypes. These findings suggest considerable differences in the breast cancer characteristics and distribution of breast cancer intrinsic subtypes in the WTC-exposed civilian population compared to that of the general population. This is important because of the known effect of molecular subtypes on breast cancer prognosis.

Identification and validation of a hub gene prognostic index for hepatocellular carcinoma

Aims: We aim to provide new insights into the mechanisms of hepatocellular carcinoma (HCC) and identify key genes as biomarkers for the prognosis of HCC. Materials & methods: Differentially expressed genes between HCC tissues and normal tissues were identified via the Gene Expression Omnibus tool. The top ten hub genes screened by the degree of the protein nodes in the protein-protein interaction network also showed significant associations with overall survival in HCC patients. Results: A prognostic model containing a five-gene signature was constructed to predict the prognosis of HCC via multivariate Cox regression analysis. Conclusion: This study identified a novel five-gene signature (CDK1, CCNB1, CCNB2, BUB1 and KIF11) as a significant independent prognostic factor.

Why Great Mitotic Inhibitors Make Poor Cancer Drugs

Chemotherapy is central to oncology, perceived to operate only on prolific cancerous tissue. Yet, many non-neoplastic tissues are more prolific compared with typical tumors. Chemotherapies achieve sufficient therapeutic windows to exert antineoplastic activity because they are prodrugs that are bioactivated in cancer-specific environments. The advent of precision medicine has obscured this concept, favoring the development of high-potency kinase inhibitors. Inhibitors of essential mitotic kinases exemplify this paradigm shift, but intolerable on-target toxicities in more prolific normal tissues have led to repeated failures in the clinic. Proliferation rates alone cannot be used to achieve cancer specificity. Here, we discuss integrating the cancer specificity of prodrugs from classical chemotherapeutics and the potency of mitotic kinase inhibitors to generate a class of high-precision cancer therapeutics.

Small-world networks of prognostic genes associated with lung adenocarcinoma development

The present study investigates the role of network topology in lung adenocarcinoma (LUAD) development. Analysis of sex- and stage-specific whole-genome expression data revealed that co-expressed and highly connected prognostic genes common to all cancer stages form a small-world network in each stage of LUAD. These small-world networks are present within stage-specific scale-free networks, conserved across the cancer stages, and linked to cancer-specific events. The presence of small-world networks across the cancer stages presents a synchronized system in the disordered environment of cancer, resulting in the evolution of malignancy. Our study reported that these small-world networks are resilient to random and systematic attacks, indicating the least opportunity to introduce perturbations by drugs as a therapeutic intervention. We concluded that highly clustered small-world networks could be controlled through transcriptional modulation for the improved treatment of LUAD.

A Translational Pipeline for Overall Survival Prediction of Breast Cancer Patients by Decision-Level Integration of Multi-Omics Data

Breast cancer is the most prevalent and among the most deadly cancers in females. Patients with breast cancer have highly variable survival rates, indicating a need to identify prognostic biomarkers. By integrating multi-omics data (e.g., gene expression, DNA methylation, miRNA expression, and copy number variations (CNVs)), it is likely to improve the accuracy of patient survival predictions compared to prediction using single modality data. Therefore, we propose to develop a machine learning pipeline using decision-level integration of multi-omics tumor data from The Cancer Genome Atlas (TCGA) to predict the overall survival of breast cancer patients. With multi-omics data consisting of gene expression, methylation, miRNA expression, and CNVs, the top performing model predicted survival with an accuracy of 85% and area under the curve (AUC) of 87%. Furthermore, the model was able to identify which modalities best contributed to prediction performance, identifying methylation, miRNA, and gene expression as the best integrated classification combination. Our method not only recapitulated several breast cancer-specific prognostic biomarkers that were previously reported in the literature but also yielded several novel biomarkers. Further analysis of these biomarkers could lend insight into the molecular mechanisms that lead to poor survival.

Gene signatures and prognostic analyses of the Tob/BTG pituitary tumor-transforming gene (PTTG) family in clinical breast cancer patients

Breast cancer is the most common cancer type in females, and exploring the mechanisms of disease progression is playing a crucial role in the development of potential therapeutics. Pituitary tumor-transforming gene (PTTG) family members are well documented to be involved in cell-cycle regulation and mitosis, and contribute to cancer development by their involvement in cellular transformation in several tumor types. The critical roles of PTTG family members as crucial transcription factors in diverse types of cancers are recognized, but how they regulate breast cancer development still remains mostly unknown. Meanwhile, a holistic genetic analysis exploring whether PTTG family members regulate breast cancer progression via the cell cycle as well as the energy metabolism-related network is lacking. To comprehensively understand the messenger RNA expression profiles of PTTG proteins in breast cancer, we herein conducted a high-throughput screening approach by integrating information from various databases such as Oncomine, Kaplan-Meier Plotter, Metacore, ClueGo, and CluePedia. These useful databases and tools provide expression profiles and functional analyses. The present findings revealed that PTTG1 and PTTG3 are two important genes with high expressions in breast cancer relative to normal breast cells, implying their unique roles in breast cancer progression. Results of our coexpression analysis demonstrated that PTTG family genes were positively correlated with thiamine triphosphate (TTP), deoxycytidine triphosphate (dCTP) metabolic, glycolysis, gluconeogenesis, and cell-cycle related pathways. Meanwhile, through Cytoscape analyzed indicated that in addition to the metastasis markers AURKA, AURKB, and NDC80, many of the kinesin superfamily (KIF) members including KIFC1, KIF2C, KIF4A, KIF14, KIF20A, KIF23, were also correlated with PTTG family transcript expression. Finally, we revealed that high levels of PTTG1 and PTTG3 transcription predicted poor survival, which provided useful insights into prospective research of cancer associated with the PTTG family. Therefore, these members of the PTTG family would serve as distinct and essential prognostic biomarkers in breast cancer.

Identification of Hub Genes Using Co-Expression Network Analysis in Breast Cancer as a Tool to Predict Different Stages

Background

Breast cancer has a high mortality rate and is the most common cancer of women worldwide. Our gene co-expression network analysis identified the genes closely related to the pathological stage of breast cancer.

Material/Methods

We performed weighted gene co-expression network analysis (WGCNA) from the Gene Expression Omnibus (GEO) database, and performed pathway enrichment analysis on genes from significant modules.

Results

A non-metastatic sample (374) of breast cancer from GSE102484 was used to construct the gene co-expression network. All 49 hub genes have been shown to be upregulated, and 19 of the 49 hub genes are significantly upregulated in breast cancer tissue. The roles of the genes CASC5, CKAP2L, FAM83D, KIF18B, KIF23, SKA1, GINS1, CDCA5, and MCM6 in breast cancer are unclear, so in order to better reveal the staging of breast cancer markers, it is necessary to study those hub genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes indicated that 49 hub genes were enriched to sister chromatid cohesion, spindle midzone, microtubule motor activity, cell cycle, and something else. Additionally, there is an independent data set – GSE20685 – for module preservation analysis, survival analysis, and gene validation.

Conclusions

This study identified 49 hub genes that were associated with pathologic stage of breast cancer, 19 of which were significantly upregulated in breast cancer. Risk stratification, therapeutic decision making, and prognosis predication might be improved by our study results. This study provides new insights into biomarkers of breast cancer, which might influence the future direction of breast cancer research.

Sp1 contributes to radioresistance of cervical cancer through targeting G2/M cell cycle checkpoint CDK1

Background/aims

Radioresistance remains a significant obstacle in the therapy of cervical cancer, and the mechanism of it is still unclear. We aimed to investigate the role of specificity protein 1 (Sp1) in radioresistance of cervical cancer.

Methods

Sp1 was examined immunohistochemically on tissues from 36 human cervical cancer patients. We used RT-qPCR and Western blot to examine the expression of Sp1 in irradiated cervical cancer cell lines SiHa and HeLa. The role of Sp1 in radioresistance of cervical cancer cells was assessed by colony-formation assay and cell cycle analysis. Dual-luciferase reporter assay was performed to detect the downstream of Sp1.

Results

High Sp1 expression was positively correlated with advanced International Federation of Gynecology and Obstetrics (FIGO) stage, lymph node metastasis, and lymphovascular space invasion (LVSI) of cervical cancer. The expression of Sp1 was dose-dependently increased in irradiated cervical cancer cell lines at both mRNA and protein levels. Colony-formation assay showed that alteration of Sp1 expression affected the survival of cervical cancer cells with radiotherapy (RT) treatment. Knockdown of Sp1 significantly strengthened the cellular response to radiation by inducing G2/M arrest in cervical cancer cells. Overexpression of Sp1 significantly decreased G2/M arrest in cervical cancer cells, which was related to upregulation of CDK1 expression. Dual-luciferase reporter assay showed the direct effect of Sp1 on the transcriptional activation of CDK1.

Conclusion

Sp1 may contribute to radioresistance through inhibiting G2/M phase arrest by targeting CDK1, and be considered as a potential therapeutic target to promote the effect of RT for patients with cervical cancer.